Lorenzo Papa

Luca Maiano, Lorenzo Papa, Ketbjano Vocaj et al.

Fake content has grown at an incredible rate over the past few years. The spread of social media and online platforms makes their dissemination on a large scale increasingly accessible by malicious actors. In parallel, due to the growing diffusion of fake image generation methods, many Deep Learning-based detection techniques have been proposed. Most of those methods rely on extracting salient features from RGB images to detect through a binary classifier if the image is fake or real. In this paper, we proposed DepthFake, a study on how to improve classical RGB-based approaches with depth-maps. The depth information is extracted from RGB images with recent monocular depth estimation techniques. Here, we demonstrate the effective contribution of depth-maps to the deepfake detection task on robust pre-trained architectures. The proposed RGBD approach is in fact able to achieve an average improvement of 3.20% and up to 11.7% for some deepfake attacks with respect to standard RGB architectures over the FaceForensic++ dataset.

L. Papa, P. Russo, I. Amerini

Ground-truth RGBD data are fundamental for a wide range of computer vision applications; however, those labeled samples are difficult to collect and time-consuming to produce. A common solution to overcome this lack of data is to employ graphic engines to produce synthetic proxies; however, those data do not often reflect real-world images, resulting in poor performance of the trained models at the inference step. In this paper we propose a novel training pipeline that incorporates Diffusion4D (D4D), a customized 4-channels diffusion model able to generate realistic RGBD samples. We show the effectiveness of the developed solution in improving the performances of deep learning models on the monocular depth estimation task, where the correspondence between RGB and depth map is crucial to achieving accurate measurements. Our supervised training pipeline, enriched by the generated samples, outperforms synthetic and original data performances achieving an RMSE reduction of (8.2%, 11.9%) and (8.1%, 6.1%) respectively on the indoor NYU Depth v2 and the outdoor KITTI dataset.

Luca Zanchetta, Lorenzo Papa, Luca Maiano et al.

Text-to-video generation is an emerging field in generative AI, enabling the creation of realistic, semantically accurate videos from text prompts. While current models achieve impressive visual quality and alignment with input text, they typically rely on static knowledge, making it difficult to incorporate new data without retraining from scratch. To address this limitation, we propose VidCLearn, a continual learning framework for diffusion-based text-to-video generation. VidCLearn features a student-teacher architecture where the student model is incrementally updated with new text-video pairs, and the teacher model helps preserve previously learned knowledge through generative replay. Additionally, we introduce a novel temporal consistency loss to enhance motion smoothness and a video retrieval module to provide structural guidance at inference. Our architecture is also designed to be more computationally efficient than existing models while retaining satisfactory generation performance. Experimental results show VidCLearn's superiority over baseline methods in terms of visual quality, semantic alignment, and temporal coherence.

Lorenzo Papa, Alessandro Sebastianelli, Gabriele Meoni et al.

Quantum computing has introduced novel perspectives for tackling and improving machine learning tasks. Moreover, the integration of quantum technologies together with well-known deep learning (DL) architectures has emerged as a potential research trend gaining attraction across various domains, such as Earth Observation (EO) and many other research fields. However, prior related works in EO literature have mainly focused on convolutional architectural advancements, leaving several essential topics unexplored. Consequently, this research investigates through three cases of study fundamental aspects of hybrid quantum machine models for EO tasks aiming to provide a solid groundwork for future research studies towards more adequate simulations and looking at the post-NISQ era. More in detail, we firstly (1) investigate how different quantum libraries behave when training hybrid quantum models, assessing their computational efficiency and effectiveness. Secondly, (2) we analyze the stability/sensitivity to initialization values (i.e., seed values) in both traditional model and quantum-enhanced counterparts. Finally, (3) we explore the benefits of hybrid quantum attention-based models in EO applications, examining how integrating quantum circuits into ViTs can improve model performance.